传统的基于梯度的图像清晰度评价函数(调焦函数)对噪声敏感, 在实际应用中易引入过多的非边缘信息, 影响系统的稳定性。本文基于光学成像系统离焦模型分析了系统离焦对图像清晰度的影响, 并提出了一种改进的图像清晰度评价方法。该方法利用最大灰度差提取细节信息来评价图像清晰度; 引入阈值区分边缘点和非边缘点来减小图像平坦区域对评价函数灵敏度的影响, 同时有效抑制噪声的干扰。进行了仿真实验和实际测试并与传统的清晰度评价函数进行了比较。结果显示, 提出的方法具有更好的灵敏度和抗噪性能, 能够准确而稳定地评价离焦模糊图像的清晰度, 可用于实际光学系统的自动调焦。

Traditional gradient based image sharpness evaluation functions are sensitive to noise, which is easy to introduce more non-edge information and shows poor stability in practical applications. This paper analyzes the effect of the defocus of an optical imaging system on image sharpness based on the defocused model and proposes a improved sharpness evaluation method. The method uses the maximum gradient as the criterion of image sharpness.Then,it introduces the optimal threshold to distinguish the edge points and non-edge points to reduce the impact of the gradient of flat area and the noise in the image on evaluation results and to suppress the noise interference. The simulation experiments and actual test are performed and obtained results are compared with that of different traditional methods. It is verified that the improved method has better sensitivity and noise immunity, which is able to evaluate the sharpness of defocused image accurately and is suitable for auto-focusing in actual optical imaging systems.